Efficient Concurrent Search Trees Using Portable Fine-Grained Locality

Abstract

Concurrent search trees are crucial data abstractions widely used in many important systems such as databases, file systems and data storage. Like other fundamental abstractions for energy-efficient computing, concurrent search trees should support both high concurrency and fine-grained data locality in a platform-independent manner. However, existing portable fine-grained locality-aware search trees such as ones based on the van Emde Boas layout (vEB-based trees) poorly support concurrent update operations while existing highlyconcurrent search trees such as non-blocking search trees do not consider fine-grained data locality. In this paper, we first present a novel methodology to achieve both portable fine-grained data locality and high concurrency for search trees. Based on the methodology, we devise a novel locality-aware concurrent search tree called GreenBST. To the best of our knowledge, GreenBST is the first practical search tree that achieves both portable fine-grained data locality and high concurrency. We analyze and compare GreenBST energy efficiency (in operations/Joule) and performance (in operations/second) with seven prominent concurrent search trees on a high performance computing (HPC) platform (Intel Xeon), an embedded platform (ARM), and an accelerator platform (Intel Xeon Phi) using parallel micro-benchmarks (Synchrobench). Our experimental results show that GreenBST achieves the best energy efficiency and performance on all the different platforms. GreenBST achieves up to 50% more energy efficiency and 60% higher throughput than the best competitor in the parallel benchmarks. These results confirm the viability of our new methodology to achieve both portable fine-grained data locality and high concurrency for search trees.